2.1 Datasets

This work utilises two datasets; email corpora belonging to single, long-term industrial projects, allowing direct comparison between two industry contexts. As this work aims to identify consistent patterns in activity, it is vital that cross-context comparison occur. Due to the high variation in scale and branch of the engineering industry, any patterns found to be consistent between datasets demonstrate generalisability of findings and the analytical approaches applied.

Comparatively limited size and dynamism in the communication networks of industry, due to, for example, changes in input from workers as the process progresses, demand consideration of the propensity for the networks under study to change (Carley Reference Carley2003; Tang et al. Reference Tang, Musolesi, Mascolo, Latora and Nicosia2010). As such, the active networks for each company must be considered, including only those members currently eligible to send or receive a topic, and hence mirroring susceptible–infected–recovered models of information diffusion (May & Lloyd Reference May and Lloyd2001; Wu et al. Reference Wu2004; Holme & Saramaki Reference Holme and Saramaki2012). Active network size was determined through monitoring those members with participation within the previous four time steps; a period of inactivity longer than this indicated a high likelihood that the member would not reappear. Summary data for each is given in Table 3.

Table 3. Dataset summary

Dataset A was taken from a large marine engineering company, relating to a long-term, large-scale engineering systems design, development, and implementation project. All members were required by policy to send all project-related emails to a project inbox, which was extracted for analysis. Emails were gathered over all stages of the process from initiation to roll-out, including 10,277 emails over 135 weeks (mean 10.8/day) between 675 involved, globally distributed members. The active network consisted of a mean of 74 members (range 6–134), with all workers having potential to input to multiple projects simultaneously.

Dataset B was significantly smaller, consisting of 1,546 emails from a small software development company, and relating to a single software development project over a 2-year period. All employees were required to archive emails in project-specific inboxes, which were copied and collated. Emails were sent or received by 78 persons, of which 6 were co-located core employees of the company within a single office, and others were nationally distributed. The frequency of emails was correspondingly low (1.98/day), but reached a mean of 10.8/day during busiest times. Employees were not working solely on this project, as indicated by bursts and stalls in email frequency (Figure 1). The active network consisted of a mean of 13 active members (range 2–28).

Figure 1. Email frequency with time; (above) Dataset A, (below) Dataset B.

Discrete stages within Dataset A were determined through interview with project members, allowing the project to be separated into specification, manufacture, sub-system testing, assembly and testing (see Figure 1A). Dataset B followed an agile methodology, as are common in software development environments (Highsmith & Cockburn Reference Highsmith and Cockburn2001; Fernandez & Fernandez Reference Fernandez and Fernandez2008), with periods of work occurring in line with deadlines for deliverables and calls for development. As such it cannot be discretised into individual stages, with boundaries instead placed during the periods of lower activity (see Figure 1B), which typically follow deadlines for deliverables and during which workers focused on projects other than that studied. Note that as stage boundaries are often fluid in nature, a 1-week overlap was assumed between each in all analysis.

Both datasets demonstrated correlation between active network size and frequency of emails sent (Dataset A, $r=$  0.734, $p<$  0.0001; Dataset B, $r=$  0.644, $p<$  0.0001), suggesting a consistency in per-engineer email frequency. Based on active network size, per-person, per-week email frequency was also consistent (Dataset A, 1.02 email per person per day; Dataset B. 1.06 emails per person per day), although these values assume an equal role of all active members each week, hence ignoring that many may lie on the periphery of project activity. While the quantity of email to be expected in a given project is highly dependent on the project context, such consistency suggests a level of normality between the two contexts in their email usage.

While company mandate dictated the storing of emails within the extracted mailboxes for each project, there is potential for workers to discuss multiple projects in a single email, hence excluding an unknown quantity of communications. Further, an unknown quantity of communication likely occurred through face to face or other means, as is typical in engineering industry (Robinson Reference Robinson2012). Quantification and capture of such further communications, through extension of the dataset, would provide further confidence and detail in the results of this work.

2.2 Topic identification

Analysis within this work is based on individual topics extracted from email content, where a topic refers to a single work area or subject under discussion. As topics have potential to be highly project-specific, this must be performed on a case-by-case basis.

Describing the specific information under discussion, the topics tracked by each metric were denoted through the appearance of specific phrases within the emails of each dataset. Numerous methods for topic identification exist (Allan Reference Allan2002; AlSumait, Barbará & Domeniconi Reference AlSumait, Barbará and Domeniconi2008; Coursey & Mihalcea Reference Coursey and Mihalcea2009; Cataldi, Caro & Schifanella Reference Cataldi, Caro and Schifanella2010), with this work utilising term frequency – cumulative inverse document frequency (tf-cidf) (Gruhl et al. Reference Gruhl, Liben-Nowell, Guha and Tomkins2004), an algorithm similar to the widely employed term frequency – inverse document frequency (tf-idf) method (Spärck Jones Reference Spärck Jones1972; Robertson Reference Robertson2004), here selected due to its ability to identify key topics within individual project timespans, and its identification of topics of varying generality. While this method omits factors such as context and semantic similarity (Brants, Chen & Tsochantaridis Reference Brants, Chen and Tsochantaridis2002; Kuhn, Ducasse & Girba Reference Kuhn, Ducasse and Girba2007; Wang, Rao & Hu Reference Wang, Rao and Hu2014), it excels in breadth of topics extracted, thereby allowing higher numbers of topics to be generated, a breadth of types of pattern in activity to be identified, and hence aligning with the core purpose of this paper. Further, topics assigned as sematic groupings by algorithms such as latent semantic analysis (Dumais et al. Reference Dumais, Furnas, Landauer, Deerwester and Harshman1988; Dumais Reference Dumais2004), although providing greater detail in the similarity and subject matter under discussion, have propensity to change in size, membership, and shape through the project timeline, thus making their consistent and robust tracking a significant challenge. While semantic topic modelling through are considered valuable, further work, term extraction through tf-cidf meets the topic identification goals of this exploratory classification, and mirrors topic and information diffusion modelling methods employed in other fields (Gruhl et al. Reference Gruhl, Liben-Nowell, Guha and Tomkins2004).

Formally, where $\mathit{tfcidf}(t)$ is document score in a given time period $t$ , $n$ is total number of time periods, and $F_{T}(t)$ is frequency of occurrence of a topic in period  $t$ :

$$\begin{eqnarray}\mathit{tfcidf}(t)=\frac{(n-1)F_{T}(t)}{\mathop{\sum }_{t=0}^{n-1}F_{T}(t)}.\end{eqnarray}$$

To generate the topic list used for analysis, the Tf-cidf algorithm was applied to each dataset. A minimum time period, $t$ , of 1 week was used to ensure sufficient email quantities for analysis. Following values used in other research (Gruhl et al. Reference Gruhl, Liben-Nowell, Guha and Tomkins2004), one-gram (single-word) topics were identified using thresholds of $\mathit{tfcidf}(t)\geqslant 12$ and $\mathit{tf}\geqslant 3$ , and bi-gram and tri-gram (two- and three-word) topics were identified from thresholds of $\mathit{tfcidf}(t)\geqslant 10$ and $\mathit{tf}\geqslant 3$ .

Emails from each mailbox were algorithmically parsed to remove metadata, duplicates, and stop words, and to extract subject line, content including signature, sender/receivers, and date sent. While not directly relevant as discussed, content email signatures typically contain company information, allowing monitoring of activity associated with companies and individual projects as extracted by the Tf-cidf algorithm.

Potential topics highlighted by the Tf-cidf algorithm were manually parsed by an engineer to produce a single topic word list, where each topic referred to a system, role, person, object, or concept within the project process, output, personnel, or management. Other terminology, such as words relating to the working lexicon of the engineers, were removed. This manual process is highly dependent on the parser, and while effort was made to be inclusive, in application to industry it is vital that those topics monitored are carefully selected from the Tf-cidf output by personnel with a breadth of experience across the sectors each project may span. Here, effort was made to include topics of varying type to encourage broad classification. Parsing produced topics as per Table 4, where an individual topic list was generated for each dataset.

Table 4. Topic identification results

2.3 Analysis metric – cumulative occurrence

The proportion of communications in a given time period that refer to a given topic give an indication of its accompanying level of activity. Those topics that demand higher proportions of communications suggest a higher focus on that particular area. Although mention in an email does not necessarily indicate that substantial work has occurred, it does indicate that the topic has been the subject of attention for multiple engineers.

Cumulative occurrence is defined as the following, where $F(t)$ is normalised cumulative occurrence of a topic in time period $t$ , $F_{e}(t)$ is number of emails in time period $t$ , and $F_{T}(t)$ is number of emails containing a topic in time period $t$ :

$$\begin{eqnarray}F(t)=\frac{F_{T}(t)}{F_{e}(t)}.\end{eqnarray}$$

Cumulative occurrence is used in this work to distinguish between topics that frequently receive attention throughout the project and those that are more rarely or briefly discussed. Topics have a slight tendency to lower values. The highest and lowest cumulative occurrence topics are given in Table 5. Whilst highest scoring topics are typically those that appear within email signatures, such as company and project names, a number of more specific topics also have higher cumulative occurrence, thus indicating work areas which typically receive higher attention.

Table 5. High and low cumulative occurrence topics, by mean value over topic life

2.4 Analysis metric – relative occurrence

All topics will have an expected occurrence for a given project with, for example, company names expected to appear more frequently than specific low-level systems (see Table 5). Deviation from this expected value may be of particular use to a manager, potentially being indicative of issues surrounding the topic, a lack of due attention, a change in general work focus, or a change in topics forming the core of the project.

As the expected occurrence level of a topic cannot easily be predicted, an estimate for a given point in time is formed through the average frequency of topic appearance over a certain prior time period, to which current usage is compared. This ratio is termed relative occurrence and is formally defined as below, where $O_{T}$ is relative occurrence of a topic, $F_{T}(t)$ is topic frequency in time period $t$ , $F_{e}(t)$ is frequency of all emails, $n$ is the current time period, $s$ is a short-term threshold describing current discussion, and $l$ is a long-term threshold used to generate the estimate of expected discussion.

$$\begin{eqnarray}O_{T}=\frac{\mathop{\sum }_{t=n-s}^{n}F_{T}(t)}{\mathop{\sum }_{t=n-s}^{n}F_{e}(t)}\cdot \frac{\mathop{\sum }_{t=n-l}^{n}F_{e}(t)}{\mathop{\sum }_{t=n-l}^{n}F_{T}(t)}.\end{eqnarray}$$

A topic is of higher-than-expected occurrence when the shorter-term proportion, $s$ , is higher than the longer term, $l$ , or $O_{T}>1$ . As projects are of varying length and pace values for $s$ and $l$ are case-dependent, for the longer-term projects studied here, a value of $s=2$ weeks has been found to be representative of recent work. The value for $l$ should be chosen to provide useful data within the specific case. When too small relative to the value of $s$ , $O_{T}>1$ frequently, typically for every rise in topic frequency that occurs (24 cases found in Figure 2A). This has the potential effect of highlighting small, background bursts of activity as a significant event. Conversely, a large $l$ in comparison to $s$ tends to a value of $l$ at the topic mean. For a topic with higher variance in occurrence through the project process, such as in Figure 2C, this provides little sensitivity at the higher and lower ends of the topic range and has potential to omit meaningful events. Here, values of $s=2$ and $l=8$ were found to be of appropriate sensitivity (see Figure 2B).

Figure 2. Effect of change in long-term threshold for topic ‘project implementation’ with short-term threshold, $s=2$ . Shaded areas indicate a value of $O(T)>1$ ; ‘*’ indicates a period in which $O_{T}>1$ . (A) 3 week length; (B) 8 week length; (C) Whole project length.

2.5 Analysis metric – occurrence duration

Each individual topic will have a varying lifespan through the project, during which it is actively discussed by workers. By identifying typical length of active discussion, where $O_{T}>1$ , and relation to patterns in discussion and project stage, project norms may be identified.

By identifying the periods through which $O_{T}>1$ , for a topic, this work identifies the lifespan of a topic over which active discussion is occurring, as opposed to periods in which topics are mentioned without significant associated worker effort. This is termed Occurrence Duration, $P_{T}$ , and is formally defined by the following, where $O_{T}(t)$ is relative occurrence in time period $t$ , $f$ is the first time period of occurrence, and $n$ is number of time periods.

$$\begin{eqnarray}P_{T}=\frac{\mathop{\sum }_{t=f}^{n}f(O_{T}(t))}{n};\quad \text{where }f\left(O_{T}(t)\right)=\left\{\begin{array}{@{}ll@{}}1,\quad & O_{T}(t)\geqslant 1\\ 0,\quad & O_{T}(t)<1.\end{array}\right.\end{eqnarray}$$

A topic with higher occurrence duration is actively discussed for a longer proportion of the project life, while a shorter duration indicates that activity is more specific to individual time periods. Duration for most is generally low. As duration increases so do the generality of the topics, describing larger components, systems, and the names of involved companies (see Table 6).

Table 6. Highest and lowest occurrence duration scores for each dataset

3.1 Background chatter pattern

For any ongoing project there is likely a certain quantity of background chatter – topics that occur at a consistent rate through longer periods. Such topics can be found in each project, and are characterised by a through-life median and mean value of $O_{T}\sim 1$ for the topic and a narrow inter-quartile range (IQR) (ie. current topic occurrence is typically close to longer-term topic occurrence). Example topics falling within this category are shown in Figure 3.

Figure 3. Background topic areas from Dataset A (above) and Dataset B (below). Line indicates longer-term occurrence. Shaded area indicates standard deviation of shorter-term occurrence.

From the data, background chatter appears either as discussion of higher-level topics (i.e., ‘Project A’, Figure 3A) or of lower-level topics that describe systems, sub-systems, or lower-level concepts (‘specification’, ‘voltage’, Figure 3A). Such patterns can also be identified in Dataset B, see Figure 3B, although with higher variability due to smaller email frequencies in each time period. In all cases, background discussion topics are those that are pervasive through the project either as a general descriptor (i.e., ‘specification’), or as a context-specific core project area (i.e., ‘voltage’).

3.2 Single spike occurrence pattern

In contrast to background chatter, some communication activity around topics occurs primarily in a single burst, here termed a spike. These topics have a high median and mean value of $O_{T}$ due to high occurrence over a short life, and vary in amplitude dependent on cumulative occurrence, $F_{T}$ , with example topics shown in Figure 4.

Figure 4. Single spike topic areas from Dataset A (above) and Dataset B (below). Shaded areas indicate a value of $O(T)>1$ .

A spike in topic activity represents high relative occurrence for a very brief time, with associated topics typically representing more specific topic areas, such as individual components, analyses and lower-level systems. For a notable spike to occur, there is a need for a significant increase in cumulative occurrence of a topic in a certain time period, implying particular focus on the topic of the project workers. This may occur as part of the normal working process, where a spike simply represents the amount of communication activity required to complete the tasks at hand. For example, test cubicle (Figure 4A) refers to the delivery of a test platform for a low-level sub-system and is discussed only through its delivery process.

In contrast, the appearance of a spike may also represent the emergence of issues around the subject matter of the topic, in which more extensive information requests or broader input from a number of personnel are required. This interpretation can be found in literature (Gruhl et al. Reference Gruhl, Liben-Nowell, Guha and Tomkins2004; Myers, Zhu & Leskovec Reference Myers, Zhu and Leskovec2012) and in this case is supported by the email content studied in each project, see quotes given in Table 7. While the impact and seriousness of such events is varied, their highlighting through spike detection could provide a valuable monitoring tool for the manager.

Table 7. Email context of single spike topics

3.2.1 Dominant spike occurrence pattern

Some topics demonstrate a spike in addition to an amount of lower level, relatively consistent communication activity. These topics are characterised by a higher-than-average occurrence duration and a higher inter-quartile range of $O_{T}$ . Here, the spike represents a need for further discussion of the topic than is typical due to specific circumstances or events in the project or the design, such as a design change or formal design meetings and delegation of actions. This is evidenced by looking in more detail at some of the email content associated with topics in Figure 5, see Table 8.

Figure 5. Dominant spike topics from Dataset A (above) and Dataset B (below). Shaded areas indicate a value of $O(T)>1$ .

In contrast to single spike topics, those following dominant spike activity display a more consistent, albeit low, level of discussion. They include more commonly occurring topics and are of broader relevance across the project timeline and hence are less likely to describe single events or bodies of work. It is plausible that, should issues or external events not have occurred, such topics would have shown similarities to background chatter across their lifespan.

Table 8. Context of topics demonstrating a dominant spike trace

3.3 Variable occurrence discussion pattern

Many topics display a more variable occurrence, with bursts of high discussion and troughs of little to none. These have varying values of cumulative and relative occurrence through their life, varying duration, and are generally characterised by the appearance of a number of spikes punctuating periods of lower activity, see Figure 6.

Figure 6. Variable occurrence topics from Dataset A (above), Dataset B (below). Shaded areas indicate a value of $O(T)>1$ .

Within some identified variable occurrence patterns there is evidence of periodicity, see project planning (Figure 6A). These regular periods of higher cumulative and relative occurrence may stem from regular events within the project – e.g., alignment with discussion of an upcoming meeting agenda and subsequent dissemination of outcomes.

Other spikes in variable occurrence patterns stem from periods in which work in that area was particularly focused. For the topic link test (Figure 6A), the spike at around week 85 occurs during the organisation and implementation of a major testing procedure operated by an external company. For the topic specification (Figure 6B), the spikes at around week 75 occur during the formation of a secondary specification for an additional interface.

These topics are characterised by inconsistency through their lifespans, with periods of higher and lower occurrence, multiple spikes punctuating periods of inactivity, and occasional periodic usage. In all cases such variance is determined by the context-specific requirements of the project, where project circumstances such as issues, higher levels of relevance, or logistical discussion have required the input of personnel in varying manners.

3.4 Summary discussion

Topic occurrence within engineering communication shows a changeable landscape, with several dynamic patterns found in communication activity (see Table 9), and with significant overlap to those identified within other fields (Gruhl et al. Reference Gruhl, Liben-Nowell, Guha and Tomkins2004).

Table 9. Patterns in topic activity traces

A number of topics align to background chatter, with a relatively consistent occurrence throughout their lifespan. These appear to represent discussion around core systems, concepts, and areas of the project that are pervasive throughout the process. These are the heart of the project – those areas that form the common threads around which the project occurs. Such topics may therefore provide potential for a manager to understand the core topics of their projects, as well as to monitor those that become more or less core over time.

In contrast to this consistency, many topics display higher occurrence bursts at certain points within their life. There are a number of patterns associated with these - a single spike amongst low-level activity, a single spike from no activity, periodic spikes, and high-variability activity. From the content of the emails under study, such spikes and variability appear to occur due to events or issues in the project, and often require broader discussion or effort from more personnel. These include standard project events such as discussion of meetings, logistics, and administration and events of more consequence, such as discussion around design changes, missing information and project issues (see Sections 3.2, 3.3 for examples).

It should be noted however that as each topic is individual and distinct, there is a challenge in delineating specific boundaries between each pattern. For example, the height at which activity is said to create a spike, or variation required for background chatter to be classified as variable discussion are potentially fluid concepts, and in reality are likely fuzzy boundaries. Further investigation is needed to identify appropriate mathematical boundaries for classification, if feasible, and to ensure correct and useful information can be provided for the manager.

4.1 Topic occurrence duration through the project

Figure 7 provides a summary of occurrence duration of individual topics through the lifespan of each project, hence representing the extent of time in which each topic had high relative occurrence, and was the subject of more focused discussion.

Figure 7. Chart of cumulative occurrence (above) and descriptive statistics (below) for topic occurrence duration. Median used as data is non-normal.

This analysis shows similarity between datasets; 50% of topics in each have an occurrence duration of less than 20% of the project length. Less than 20% exceeded 40% in duration, while none exceeded 60.0%. Periods of high occurrence for a typical topic can therefore be considered to be relatively short-lived, with most displaying low levels of activity through the majority of the project. Table 10 provides examples of topics of varying occurrence duration within each dataset.

Table 10. Example topics of different occurrence durations

4.2 Multiple-stage topic occurrence

Across both datasets a large number of topics persist between and across process stages. Although some relation may be expected due to the distinct purposes of different stages and the tasks within (Pahl & Beitz Reference Pahl and Beitz1984; Hales Reference Hales1987; Pugh Reference Pugh1990), this demonstrates that many topics are not stage-bound, but are of relevance across the project.

A majority of topics are first raised in the initial two stages of the project, but are not resolved until later (85.8% Dataset A, 69.2% Dataset B emerging in first two stages; 8.76% Dataset A; 7.69% Dataset B disappearing in first two stages; see Table 11), perhaps reflecting planning work and suggesting that the majority of important topics within a project are raised early-on for future resolution. Further, very few topics are first raised in the last project stage (0.302% Dataset A, 0.00% Dataset B, Table 11), suggesting that the work that occurs here is typically not new, but rather foreshadowed by that in earlier project stages.

Table 11. Topic occurrence in project stages

4.3 Stage localisation of topics

Many topics are only discussed within smaller segments of the project process. Taking the process stage boundaries in each dataset, the occurrence duration of each topic can be used to identify those for which discussion was primarily isolated within a single stage, was spread strongly amongst multiple stages, and those with more varied occurrence over the project life. A topic is defined as isolated to one or more stages by proportional occurrence in excess of that appearing in any combination of other stages. A topic is considered isolated in $n$ stages if (a) the proportion of its occurrence duration within each stage of the isolation group, $P_{i}$ , is greater than the combined proportion of all stages outside of the group, $P_{o}$ , by a factor, $f$ , of $(n+1)$ , and (b) the topic is not part of an isolation group for any larger value of $n$ :

$$\begin{eqnarray}P_{i}\geqslant \frac{P_{o}f}{n};\quad \text{where }f=n+1.\end{eqnarray}$$

The value of $f$ chosen here allows maximum tolerance for lower-level discussion in other stages while ensuring a majority in isolated stages, thus allowing interpretation of topic/stage relationships. In industry application, a value that represents isolation should be determined in the specific context. Figure 8 shows topics from Dataset A that are isolated within one (commercial proposal – specification stage; Company 1A2 – sub-system testing) and two stages (Company Acronym 1C2 – assembly and testing).

Figure 8. Isolated topics within Dataset A.

In each dataset the majority of topics are not isolated to any combination of stages, with a variable relative occurrence across the process (64.7% Dataset A, 52.3% Dataset B; Table 12). This supports the finding that topic discussion is not typically stage-specific, but rather is of relevance throughout the project process. With that said, even when non-isolated to project stages, the majority of high relative occurrence in each dataset occurs during central stages (Manufacture – Assembly, Dataset A; Stage 2 – Stage 4, Dataset B; Table 12), indicating that these stages contain the highest levels of focused work and a wider breadth of work areas. That very few topics are equally important in many stages (3/4/5 stage focused topics; Table 12) suggests that background chatter topics, despite generally consistent in appearance, will show variation that is not coincident with stage boundaries. The peak seen for Company Acronym 1C2 in Figure 8 is likely due to the decrease in email transmission at the end of the project, thus inflating the relative proportion of topics sent from the company that remained active.

Table 12. Proportion of isolated topics

When showing some traits of isolation, and hence more likely to be of specific relevance to the stages in which they appear, most topics belong to either one stage (18.4% Dataset A, 37.7% Dataset B; Table 12) or two (10.3% Dataset A, 8.46% Dataset B; Table 12), and typically belong to manufacture/testing (Dataset A) or stages 2/3 (Dataset B) – the central period of the project process. It is therefore plausible that in this period the work performed aligns with the specific purposes of the relevant process stages. Further, it implies that the topics within these stages are more often short-lived, following a spike pattern, and hence are likely to describe lower-level systems.

Within Dataset B, a predominance of isolated and emerging topics can be found in stage 4 which, when studying the textual content of the emails, typically refer to terminology surrounding a complex software testing regime spread across multiple companies and systems. Their formation and discussion is a timely response to the needs of the project, indicating that larger bodies of work may initiate a new period of topic creation. That such topics were not raised in earlier stages could be a result of the more agile process methodology of the software development project, a lack of necessity for specific technical language before work commenced, or potentially a deficiency in appropriate planning of the testing procedure. Indeed, when looking at the content of the emails sent surrounding these topics, there is evidence of significant querying, organisation between distributed parties and smaller issue resolution (see Table 13).

Table 13. Context of emails including fare query topic, isolated in stage 4; Dataset B

4.4 Summary discussion

This section has presented traits of topic occurrence in relation to project process stages, through the measurement of the periods in which relative occurrence was high. While a large amount of variation can be found between topics, the findings do suggest a typical pattern and set of characteristics to the topic discussion within an engineering project, which have potential to be of direct use to a project manager in their work.

Topics with a higher occurrence duration (above ${\sim}$ 20%, Section 4.1) show a tendency towards high-level and core project systems and concepts. This aligns with individual topic patterns identified, specifically the longer-lived and core subjects apparent in background chatter.

There is little evidence for a strong stage-specificity in the occurrence of topic discussion when following stages delineated by project members. Despite the variation in purpose of different stages and the types of task that each require, communication activity associated with specific topics appears to transcend stage boundaries, indicating that the tasks to which many individual topics are subject encompass those common in multiple process stages. Interestingly, and a subject for future work, that topics do not conform to stage boundaries may also act as commentary on the quality and reality of boundaries set by personnel within a project. The characteristics of topic activity presented here allow the building of general trends of topic activity for engineering projects, see Table 14.

Table 14. Characteristics of topic communication activity through the project process